Overview

Dataset statistics

Number of variables12
Number of observations3613
Missing cells2807
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory338.8 KiB
Average record size in memory96.0 B

Variable types

NUM11
CAT1

Warnings

country has a high cardinality: 178 distinct values High cardinality
basic_sanitation_access_percent has 498 (13.8%) missing values Missing
suicide_per_100000_people has 2309 (63.9%) missing values Missing

Reproduction

Analysis started2022-03-29 18:18:14.734344
Analysis finished2022-03-29 18:18:31.906927
Duration17.17 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

country
Categorical

HIGH CARDINALITY

Distinct178
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
Singapore
 
27
Croatia
 
27
Netherlands
 
27
Sweden
 
27
Malta
 
27
Other values (173)
3478 
ValueCountFrequency (%) 
Singapore270.7%
 
Croatia270.7%
 
Netherlands270.7%
 
Sweden270.7%
 
Malta270.7%
 
Slovenia270.7%
 
Ukraine270.7%
 
Norway270.7%
 
Estonia270.7%
 
Portugal270.7%
 
Other values (168)334392.5%
 
2022-03-29T15:18:31.979641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-29T15:18:32.070754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length7
Mean length8.367284805
Min length3

year
Real number (ℝ≥0)

Distinct27
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.762801
Minimum1990
Maximum2016
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:32.137225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1993
Q12001
median2006
Q32011
95-th percentile2015
Maximum2016
Range26
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.781467002
Coefficient of variation (CV)0.00338099151
Kurtosis-0.6258460842
Mean2005.762801
Median Absolute Deviation (MAD)5
Skewness-0.4129609357
Sum7246821
Variance45.9882947
MonotocityNot monotonic
2022-03-29T15:18:32.204621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
20141784.9%
 
20131784.9%
 
20111784.9%
 
20101784.9%
 
20121784.9%
 
20151774.9%
 
20091774.9%
 
20071764.9%
 
20061764.9%
 
20081764.9%
 
Other values (17)184151.0%
 
ValueCountFrequency (%) 
1990541.5%
 
1991571.6%
 
1992601.7%
 
1993591.6%
 
1994571.6%
 
ValueCountFrequency (%) 
20161744.8%
 
20151774.9%
 
20141784.9%
 
20131784.9%
 
20121784.9%
 

basic_sanitation_access_percent
Real number (ℝ≥0)

MISSING

Distinct846
Distinct (%)27.2%
Missing498
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean70.64791332
Minimum3.4
Maximum100
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:32.280343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile13.17
Q144.05
median85.6
Q397.6
95-th percentile100
Maximum100
Range96.6
Interquartile range (IQR)53.55

Descriptive statistics

Standard deviation30.77317561
Coefficient of variation (CV)0.4355850607
Kurtosis-0.9446136345
Mean70.64791332
Median Absolute Deviation (MAD)13.9
Skewness-0.7549232893
Sum220068.25
Variance946.9883374
MonotocityNot monotonic
2022-03-29T15:18:32.359677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1002135.9%
 
99.1621.7%
 
99.9571.6%
 
98.8361.0%
 
99.6330.9%
 
99.4300.8%
 
97.9290.8%
 
98280.8%
 
99.5280.8%
 
98.1270.7%
 
Other values (836)257271.2%
 
(Missing)49813.8%
 
ValueCountFrequency (%) 
3.41< 0.1%
 
3.641< 0.1%
 
3.881< 0.1%
 
4.111< 0.1%
 
4.321< 0.1%
 
ValueCountFrequency (%) 
1002135.9%
 
99.9571.6%
 
99.8100.3%
 
99.7110.3%
 
99.6330.9%
 
Distinct1393
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.84452809
Minimum1.85
Maximum234
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:32.437305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.78
Q17.94
median19.5
Q355.4
95-th percentile128
Maximum234
Range232.15
Interquartile range (IQR)47.46

Descriptive statistics

Standard deviation41.62014388
Coefficient of variation (CV)1.099766491
Kurtosis1.981740859
Mean37.84452809
Median Absolute Deviation (MAD)13.91
Skewness1.573217287
Sum136732.28
Variance1732.236376
MonotocityNot monotonic
2022-03-29T15:18:32.514300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
101150.4%
 
11.2130.4%
 
15.7130.4%
 
17.6120.3%
 
107120.3%
 
16.7120.3%
 
10.9120.3%
 
10.2120.3%
 
10.6120.3%
 
19.2110.3%
 
Other values (1383)348996.6%
 
ValueCountFrequency (%) 
1.851< 0.1%
 
2.021< 0.1%
 
2.031< 0.1%
 
2.11< 0.1%
 
2.151< 0.1%
 
ValueCountFrequency (%) 
2341< 0.1%
 
2291< 0.1%
 
2261< 0.1%
 
2231< 0.1%
 
21720.1%
 
Distinct594
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.847794077
Minimum0.95
Maximum7.68
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:32.591527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile1.3
Q11.66
median2.29
Q33.78
95-th percentile5.88
Maximum7.68
Range6.73
Interquartile range (IQR)2.12

Descriptive statistics

Standard deviation1.5214474
Coefficient of variation (CV)0.5342547106
Kurtosis0.05269051893
Mean2.847794077
Median Absolute Deviation (MAD)0.77
Skewness1.039316331
Sum10289.08
Variance2.314802192
MonotocityNot monotonic
2022-03-29T15:18:32.668995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.34270.7%
 
1.47270.7%
 
1.31260.7%
 
1.58260.7%
 
1.38260.7%
 
1.6260.7%
 
1.72260.7%
 
1.8250.7%
 
1.98250.7%
 
1.73240.7%
 
Other values (584)335592.9%
 
ValueCountFrequency (%) 
0.951< 0.1%
 
0.9620.1%
 
0.9720.1%
 
0.981< 0.1%
 
0.991< 0.1%
 
ValueCountFrequency (%) 
7.681< 0.1%
 
7.671< 0.1%
 
7.661< 0.1%
 
7.641< 0.1%
 
7.631< 0.1%
 

co2_emissions_tonnes_per_person
Real number (ℝ≥0)

Distinct1664
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.228570662
Minimum0.0159
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:32.751355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0159
5-th percentile0.09474
Q10.752
median2.99
Q37.72
95-th percentile17.64
Maximum67.1
Range67.0841
Interquartile range (IQR)6.968

Descriptive statistics

Standard deviation6.591078883
Coefficient of variation (CV)1.260589042
Kurtosis14.68597445
Mean5.228570662
Median Absolute Deviation (MAD)2.708
Skewness2.964992556
Sum18890.8258
Variance43.44232085
MonotocityNot monotonic
2022-03-29T15:18:32.827322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.2160.4%
 
10.1150.4%
 
10.3150.4%
 
11.3130.4%
 
1.04130.4%
 
10.5120.3%
 
11120.3%
 
1.01120.3%
 
1.18110.3%
 
1.11100.3%
 
Other values (1654)348496.4%
 
ValueCountFrequency (%) 
0.01591< 0.1%
 
0.01711< 0.1%
 
0.0191< 0.1%
 
0.02021< 0.1%
 
0.02091< 0.1%
 
ValueCountFrequency (%) 
67.11< 0.1%
 
63.61< 0.1%
 
61.81< 0.1%
 
60.61< 0.1%
 
58.61< 0.1%
 

employment_rate_percent
Real number (ℝ≥0)

Distinct530
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.09092167
Minimum28.9
Maximum87.8
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:32.907708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28.9
5-th percentile39.4
Q149.9
median57.1
Q363.2
95-th percentile77.64
Maximum87.8
Range58.9
Interquartile range (IQR)13.3

Descriptive statistics

Standard deviation11.02380242
Coefficient of variation (CV)0.1930920381
Kurtosis-0.04917632953
Mean57.09092167
Median Absolute Deviation (MAD)6.6
Skewness0.2012949482
Sum206269.5
Variance121.5242199
MonotocityNot monotonic
2022-03-29T15:18:32.983658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
58.2280.8%
 
58270.7%
 
60230.6%
 
52.6220.6%
 
55.1220.6%
 
59.2220.6%
 
59.4220.6%
 
58.8220.6%
 
57.4210.6%
 
56.5210.6%
 
Other values (520)338393.6%
 
ValueCountFrequency (%) 
28.920.1%
 
29.21< 0.1%
 
29.320.1%
 
29.41< 0.1%
 
29.61< 0.1%
 
ValueCountFrequency (%) 
87.81< 0.1%
 
87.41< 0.1%
 
87.11< 0.1%
 
871< 0.1%
 
86.81< 0.1%
 

hdi
Real number (ℝ≥0)

Distinct639
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6813606421
Minimum0.253
Maximum0.953
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:33.064355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.253
5-th percentile0.403
Q10.565
median0.705
Q30.806
95-th percentile0.902
Maximum0.953
Range0.7
Interquartile range (IQR)0.241

Descriptive statistics

Standard deviation0.158158554
Coefficient of variation (CV)0.2321216464
Kurtosis-0.7196891619
Mean0.6813606421
Median Absolute Deviation (MAD)0.115
Skewness-0.4487690133
Sum2461.756
Variance0.02501412821
MonotocityNot monotonic
2022-03-29T15:18:33.140361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.899200.6%
 
0.724170.5%
 
0.681160.4%
 
0.72160.4%
 
0.767150.4%
 
0.728150.4%
 
0.666150.4%
 
0.835140.4%
 
0.702140.4%
 
0.8140.4%
 
Other values (629)345795.7%
 
ValueCountFrequency (%) 
0.2531< 0.1%
 
0.2591< 0.1%
 
0.2631< 0.1%
 
0.2681< 0.1%
 
0.2761< 0.1%
 
ValueCountFrequency (%) 
0.9531< 0.1%
 
0.9511< 0.1%
 
0.9481< 0.1%
 
0.9461< 0.1%
 
0.9451< 0.1%
 
Distinct1317
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18668.31414
Minimum563
Maximum115000
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:33.220909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum563
5-th percentile1356
Q14160
median11500
Q329000
95-th percentile54340
Maximum115000
Range114437
Interquartile range (IQR)24840

Descriptive statistics

Standard deviation19098.43275
Coefficient of variation (CV)1.023040035
Kurtosis2.912245621
Mean18668.31414
Median Absolute Deviation (MAD)8630
Skewness1.600439221
Sum67448619
Variance364750133.5
MonotocityNot monotonic
2022-03-29T15:18:33.298930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11900190.5%
 
10300180.5%
 
11000180.5%
 
10500180.5%
 
10600170.5%
 
11700170.5%
 
11300160.4%
 
10100150.4%
 
11800140.4%
 
10400130.4%
 
Other values (1307)344895.4%
 
ValueCountFrequency (%) 
5631< 0.1%
 
5781< 0.1%
 
6311< 0.1%
 
6871< 0.1%
 
7151< 0.1%
 
ValueCountFrequency (%) 
1150001< 0.1%
 
11300020.1%
 
1120001< 0.1%
 
1110001< 0.1%
 
1090001< 0.1%
 

life_expectancy
Real number (ℝ≥0)

Distinct386
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.63243842
Minimum32.5
Maximum84.7
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:33.377963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum32.5
5-th percentile54.4
Q165.5
median72.6
Q377
95-th percentile81.5
Maximum84.7
Range52.2
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation8.461934475
Coefficient of variation (CV)0.1198023835
Kurtosis0.2199454916
Mean70.63243842
Median Absolute Deviation (MAD)5.3
Skewness-0.8423724842
Sum255195
Variance71.60433505
MonotocityNot monotonic
2022-03-29T15:18:33.456409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
75.5320.9%
 
75270.7%
 
76.8270.7%
 
76.3260.7%
 
78260.7%
 
77.8260.7%
 
75.3260.7%
 
76.5250.7%
 
74.7250.7%
 
76250.7%
 
Other values (376)334892.7%
 
ValueCountFrequency (%) 
32.51< 0.1%
 
43.11< 0.1%
 
43.31< 0.1%
 
43.81< 0.1%
 
43.930.1%
 
ValueCountFrequency (%) 
84.720.1%
 
84.51< 0.1%
 
84.41< 0.1%
 
84.31< 0.1%
 
84.130.1%
 

population_total
Real number (ℝ≥0)

Distinct1760
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36691358.18
Minimum98000
Maximum1420000000
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:33.533881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum98000
5-th percentile333200
Q13110000
median8680000
Q326500000
95-th percentile128000000
Maximum1420000000
Range1419902000
Interquartile range (IQR)23390000

Descriptive statistics

Standard deviation132341540.4
Coefficient of variation (CV)3.606885845
Kurtosis78.20505012
Mean36691358.18
Median Absolute Deviation (MAD)7320000
Skewness8.542734314
Sum1.325658771e+11
Variance1.751428333e+16
MonotocityNot monotonic
2022-03-29T15:18:33.609846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10300000280.8%
 
10400000250.7%
 
10500000190.5%
 
10600000190.5%
 
11200000190.5%
 
10200000180.5%
 
128000000160.4%
 
1990000160.4%
 
11300000150.4%
 
5400000150.4%
 
Other values (1750)342394.7%
 
ValueCountFrequency (%) 
980001< 0.1%
 
985001< 0.1%
 
990001< 0.1%
 
996001< 0.1%
 
1000001< 0.1%
 
ValueCountFrequency (%) 
14200000001< 0.1%
 
141000000020.1%
 
14000000001< 0.1%
 
13900000001< 0.1%
 
138000000020.1%
 

suicide_per_100000_people
Real number (ℝ≥0)

MISSING

Distinct620
Distinct (%)47.5%
Missing2309
Missing (%)63.9%
Infinite0
Infinite (%)0.0%
Mean11.84534363
Minimum0.0454
Maximum44.3
Zeros0
Zeros (%)0.0%
Memory size28.2 KiB
2022-03-29T15:18:33.693267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0454
5-th percentile1.749
Q16.555
median10.9
Q314.8
95-th percentile26.985
Maximum44.3
Range44.2546
Interquartile range (IQR)8.245

Descriptive statistics

Standard deviation7.573063358
Coefficient of variation (CV)0.6393282957
Kurtosis2.208666907
Mean11.84534363
Median Absolute Deviation (MAD)4.235
Skewness1.242623266
Sum15446.3281
Variance57.35128862
MonotocityNot monotonic
2022-03-29T15:18:33.767064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.4160.4%
 
10.7150.4%
 
10.8140.4%
 
13.6140.4%
 
11.3140.4%
 
13.7140.4%
 
13140.4%
 
13.1130.4%
 
12.2130.4%
 
11.2130.4%
 
Other values (610)116432.2%
 
(Missing)230963.9%
 
ValueCountFrequency (%) 
0.04541< 0.1%
 
0.05781< 0.1%
 
0.06281< 0.1%
 
0.06691< 0.1%
 
0.06841< 0.1%
 
ValueCountFrequency (%) 
44.31< 0.1%
 
43.61< 0.1%
 
43.21< 0.1%
 
42.51< 0.1%
 
42.41< 0.1%
 

Interactions

2022-03-29T15:18:22.011299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.107091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.181367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.259020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.336924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.419543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.497470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.572136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.671417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.748144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.823887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.896193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:22.963150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.027179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.098905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.167641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.235176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.308739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.375734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.450151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.518264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.584707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.648247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.737886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.810306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:23.889491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.226571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.304632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.376465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.449188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.526661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.601956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.676585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.748279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.819494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.888385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:24.970753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.060453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.144876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.219388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.297794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.383355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.473928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.586420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.664375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.748620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.832837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:25.937247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.038237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.121757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.197024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.269156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.351552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.424540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.497213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.570963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.643869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.712685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.787347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.858903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.927348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:26.991867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.054398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.124865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.189279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.255210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.318068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.382204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.442846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.509974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.574061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.638464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.698960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.756764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.824155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.886419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:27.949442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.011534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.083634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.153209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.229011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.301906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.378197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.451529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.523184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.602459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.675046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.749036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.818329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.888584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:28.955428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.027714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.097189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.167049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.232995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.297913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.371856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.443911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.512943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.579343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.872866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:29.941414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.015414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.086095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.157215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.224489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.289471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.362232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.433798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.512630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.592852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.665207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.735448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.811053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.883075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:30.963374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.038422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.110341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.203988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.285512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.358866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-29T15:18:33.834438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-29T15:18:33.973319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-29T15:18:34.089112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-29T15:18:34.204100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-29T15:18:31.511133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.675701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.783667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T15:18:31.835526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

countryyearbasic_sanitation_access_percentchild_mortality_0_5_year_olds_dying_per_1000_bornchildren_per_woman_total_fertilityco2_emissions_tonnes_per_personemployment_rate_percenthdiincome_per_person_gdppercapita_ppp_inflation_adjustedlife_expectancypopulation_totalsuicide_per_100000_people
0Afghanistan199923.5129.07.490.037045.90.34557854.720800000NaN
1Afghanistan200023.5125.07.390.037645.90.34756354.821600000NaN
2Afghanistan200124.6121.07.270.047147.70.378119055.522600000NaN
3Afghanistan200225.8117.07.140.050947.90.387124056.523700000NaN
4Afghanistan200326.9113.06.990.036847.50.400120057.124700000NaN
5Afghanistan200428.0109.06.830.051548.50.410129057.625700000NaN
6Afghanistan200529.2104.06.650.062247.90.419132058.026400000NaN
7Afghanistan200630.4100.06.460.083848.20.431146058.527100000NaN
8Afghanistan200731.796.06.250.152047.40.436148059.227700000NaN
9Afghanistan200832.991.96.040.238048.30.447176059.928400000NaN

Last rows

countryyearbasic_sanitation_access_percentchild_mortality_0_5_year_olds_dying_per_1000_bornchildren_per_woman_total_fertilityco2_emissions_tonnes_per_personemployment_rate_percenthdiincome_per_person_gdppercapita_ppp_inflation_adjustedlife_expectancypopulation_totalsuicide_per_100000_people
3603Zimbabwe200742.597.04.010.62478.60.432174048.912400000NaN
3604Zimbabwe200841.892.74.020.44178.40.448193050.212500000NaN
3605Zimbabwe200941.186.34.030.60778.30.472227052.312700000NaN
3606Zimbabwe201040.379.04.020.73778.30.490256054.412900000NaN
3607Zimbabwe201139.669.84.000.58778.50.516293056.013100000NaN
3608Zimbabwe201238.962.33.960.87278.70.527294057.213400000NaN
3609Zimbabwe201338.357.53.900.88178.90.537296058.013600000NaN
3610Zimbabwe201437.654.33.840.88179.00.544296058.613800000NaN
3611Zimbabwe201536.950.43.760.77179.10.549294059.214000000NaN
3612Zimbabwe201636.249.33.680.84579.40.553303059.914200000NaN